Follow Datanami:
March 24, 2020

The First Step to Success for the Chief Data Officer: Changing Your Outlook on Business Value

Kevin M. Lewis

(SFIO CRACHO/Shutterstock)

The modern enterprise no longer needs to worry about obtaining the necessary data to understand their customer’s habits and needs. The issue that companies grapple with today is rooted in the required scale and agility to analyze all the available data at their fingertips. Add in the costs of analytics infrastructure and the technology skills gap and it becomes clear why many companies struggle to create a big enough return on their data investments to stay afloat in an ultra-competitive market.

Enter the Chief Data Officer. Created in 2002, the CDO position was established to enable companies to manage and rationalize data across the enterprise. Unfortunately, the role and the evolution of a CDO’s critical responsibility has introduced new issues to fundamental data analytics processes.

The Biggest Mistake CDOs Are Making

Although created to crystallize the waters of enterprise data, in many cases, the CDO position has done the opposite, further muddling the perception of data and its uses. In response, many consulting organizations have weighed in about what’s going wrong and how to solve data issues in a way that lasts. For example, most advice to CDOs comes down to following some version of the below three steps:

  1. Create an enterprise data strategy, treating data as an enterprise asset.
  2. Ensure that the data strategy is focused on business value (e.g., increasing revenue and improving cost control) as well as on risk management (e.g., inclusive of compliance and privacy).
  3. Implement the strategy incrementally, creating a “foundation” of trusted data bit-by-bit.

    (OpturaDesign/Shutterstock)

Although at first this may appear as good advice, it assumes that it’s the job of the CDO to propose business value directly, rather than to support the business value that others in the organization have already achieved through sponsorship. Virtually all major business initiatives — regardless of the sponsor — require data and analytics to succeed. If the CDO proposes his or her own value in his or her own projects, there is often nobody providing the data needed for those other business initiatives. The business initiatives will continue to proliferate data while the CDO builds a “foundation” of data, which results in yet another silo.

The right approach for this situation is simple. In fact, it’s so simple and subtle that it’s easy to confuse with the typical advice that CDOs have heard over and over again. However, there is a crucial difference.

The Right Approach

When CDOs start to think about themselves as part of a whole rather than a sole entity, their unique abilities to enhance business value through data start to fall into place as they focus their efforts across the organization. By following these three key steps, the CDO can uplevel their role and assist in helping their organization realize the benefits of data.

  1. Identify the already-planned and already-funded (or proposed to be funded) business initiatives of the company. This is the crucial, subtle difference in typical CDO advice that is easy to miss. This critical first step does not suggest finding business value or aligning to the company strategy, values or culture; nor does it suggest finding of proposing data or analytics initiatives. Instead, it asks CDOs to simply identify the planned, or about-to-be-planned, initiatives of the company. This step appears very easy — and it is — but it’s almost never done, and it is the most important step in driving success.

    (pgraphis/Shutterstock)

  2. Propose opportunities to utilize data that support existing efforts across teams.  We repeat: the CDO does not have to propose business value. The CDO simply proposes work that directly contributes to the value proposed by business initiatives that are sponsored by others within the company. If the CDO wishes to advocate for the innovative use of data and analytics, it should be to influence initiatives that are sponsored by others, not the CDO’s own ideas. And because the CDO is looking across initiatives, the data deployment can be planned rationally, as many seemingly unrelated or semi-related initiatives need the same data (such as for departments dealing with sales, products, customers, sensors, parts, employees and so on).
  3. Deploy data just-in-time and just-enough and in just the right condition (i.e., quality that is not perfect, but “fit for purpose”) to meet the needs of the initiatives. In this way, the focus of the data strategy is to deliver the data needed by applications within business initiatives — no more, and no less. Data quality will never be 100 percent perfect, so data issues must also be prioritized. Specific initiatives will have specific data attribute and quality requirements, so there will be a basis for scope management. By instituting good architecture and design practices, each data deployment can be shaped like a small puzzle piece — deployed quickly, but set up to fit with other data, thus building coherence piece by piece and turning the proliferation problem around.

For many companies, the CDO role has turned into a vicious cycle in which CDOs try to propose business value in ways that advances the common belief that CDOs are not providing enough return. By taking a step back from the maze of conflicting counsel for how CDOs can promote and maintain their own value within an organization, CDOs should ignore bad advice on how to prove their value and instead focus on creating cohesion on data within their organization.

By aligning the supply of data with the already-funded or already-planned initiatives of the company, the CDO enables the enterprise to maintain a smooth, clear data analytics strategy that ensures no collection or analysis of data is wasted.

About the Author: In his role as Director of Data and Architecture Strategy for Teradata, Kevin M. Lewis works with clients across all major industries. Kevin helps large enterprises transform and modernize data and analytics programs including organization, process, and architecture. The practice advocates strategies that deliver value quickly while simultaneously building a coherent ecosystem with each step.

Do NOT follow this link or you will be banned from the site!
Share This